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        <span>重要特征查看</span>
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        <h1 id="重要特征查看"><a href="#重要特征查看" class="headerlink" title="重要特征查看"></a>重要特征查看</h1><p>目的：将睡眠分期中的345阶段的睡眠分期结果进行比较，查看特征对睡眠分期的影响,</p>
<p>筛选出特征贡献度最高的那几个，进行查看</p>
<p>程序：利用表格形式输出特征的排序</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/6/18</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 选定固定的特征值</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> h <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">4</span>):</span><br><span class="line">    feature_import = pd.read_excel(<span class="string">&#x27;E:/features&#x27;</span> + <span class="string">&#x27;/feature_important&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % h + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">    df = pd.get_dummies(feature_import.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature_import), <span class="number">1</span>:<span class="number">23</span>])</span><br><span class="line">    data = df.T</span><br><span class="line">    print(<span class="string">f&#x27;列写特征重要度排序&#x27;</span>, end=<span class="string">&#x27;\t&#x27;</span>)</span><br><span class="line">    print()</span><br><span class="line">    print(<span class="string">f&#x27;序号&#x27;</span>, end=<span class="string">&#x27;\t\t&#x27;</span>)</span><br><span class="line">    print(<span class="string">f&#x27;特征&#x27;</span>, end=<span class="string">&#x27;\t\t&#x27;</span>)</span><br><span class="line">    print(<span class="string">f&#x27;重要度&#x27;</span>)</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">22</span>):</span><br><span class="line">        print(<span class="string">f&#x27;%2d&#x27;</span> % i, end=<span class="string">&#x27;\t&#x27;</span>)</span><br><span class="line">        print(<span class="string">f&#x27;%10s&#x27;</span> % df.keys()[i], end=<span class="string">&#x27;\t&#x27;</span>)</span><br><span class="line">        print(<span class="string">f&#x27;%0.3f&#x27;</span> % data[<span class="number">0</span>][i])</span><br></pre></td></tr></table></figure>

<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line">序号		特征	 重要度</span><br><span class="line"> <span class="number">0</span>	    <span class="number">5</span>pNN20	<span class="number">0.021</span></span><br><span class="line"> <span class="number">1</span>	     <span class="number">5</span>NN20	<span class="number">0.021</span></span><br><span class="line"> <span class="number">2</span>	    <span class="number">5</span>pNN50	<span class="number">0.019</span></span><br><span class="line"> <span class="number">3</span>	     <span class="number">5</span>NN50	<span class="number">0.019</span></span><br><span class="line"> <span class="number">4</span>	  <span class="number">5</span>p_RMSSD	<span class="number">0.018</span></span><br><span class="line"> <span class="number">5</span>	    <span class="number">5</span>p_var	<span class="number">0.018</span></span><br><span class="line"> <span class="number">6</span>	    <span class="number">5</span>csi50	<span class="number">0.017</span></span><br><span class="line"> <span class="number">7</span>	    <span class="number">5</span>csi30	<span class="number">0.017</span></span><br><span class="line"> <span class="number">8</span>	    <span class="number">5</span>csi10	<span class="number">0.017</span></span><br><span class="line"> <span class="number">9</span>	       <span class="number">5</span>HF	<span class="number">0.017</span></span><br><span class="line"><span class="number">10</span>	   <span class="number">5</span>p_skew	<span class="number">0.017</span></span><br><span class="line"><span class="number">11</span>	   <span class="number">5</span>p_SDNN	<span class="number">0.017</span></span><br><span class="line"><span class="number">12</span>	 <span class="number">5</span>p_median	<span class="number">0.017</span></span><br><span class="line"><span class="number">13</span>	   <span class="number">5</span>R_mean	<span class="number">0.016</span></span><br><span class="line"><span class="number">14</span>	  <span class="number">5</span>HR_mean	<span class="number">0.016</span></span><br><span class="line"><span class="number">15</span>	    <span class="number">5</span>p_RMS	<span class="number">0.015</span></span><br><span class="line"><span class="number">16</span>	   <span class="number">5</span>p_mean	<span class="number">0.015</span></span><br><span class="line"><span class="number">17</span>	    <span class="number">5</span>p_max	<span class="number">0.014</span></span><br><span class="line"><span class="number">18</span>	 <span class="number">5</span>R_median	<span class="number">0.014</span></span><br><span class="line"><span class="number">19</span>	     <span class="number">5</span>apen	<span class="number">0.014</span></span><br><span class="line"><span class="number">20</span>	<span class="number">5</span>p_peak_factor	<span class="number">0.014</span></span><br><span class="line"><span class="number">21</span>	   <span class="number">5</span>csi100	<span class="number">0.014</span></span><br></pre></td></tr></table></figure>

<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line">序号		特征	 重要度</span><br><span class="line"> 0	     5NN20	0.024</span><br><span class="line"> 1	    5pNN20	0.023</span><br><span class="line"> 2	    5pNN50	0.023</span><br><span class="line"> 3	     5NN50	0.022</span><br><span class="line"> 4	   5p_skew	0.020</span><br><span class="line"> 5	       5HF	0.018</span><br><span class="line"> 6	 5p_median	0.017</span><br><span class="line"> 7	   5R_mean	0.016</span><br><span class="line"> 8	   5p_SDNN	0.016</span><br><span class="line"> 9	    5p_var	0.016</span><br><span class="line">10	    5p_RMS	0.016</span><br><span class="line">11	    5csi10	0.016</span><br><span class="line">12	  5p_RMSSD	0.016</span><br><span class="line">13	   5R_CVSD	0.016</span><br><span class="line">14	    5p_max	0.016</span><br><span class="line">15	    5csi50	0.016</span><br><span class="line">16	   5R_SDSD	0.015</span><br><span class="line">17	   5p_mean	0.015</span><br><span class="line">18	      5sd1	0.015</span><br><span class="line">19	  5R_RMSSD	0.015</span><br><span class="line">20	   5HR_min	0.015</span><br><span class="line">21	     5apen	0.015</span><br></pre></td></tr></table></figure>

<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line">序号		特征	 重要度</span><br><span class="line"> <span class="number">0</span>	    <span class="number">5</span>pNN20	<span class="number">0.026</span></span><br><span class="line"> <span class="number">1</span>	     <span class="number">5</span>NN20	<span class="number">0.025</span></span><br><span class="line"> <span class="number">2</span>	    <span class="number">5</span>pNN50	<span class="number">0.023</span></span><br><span class="line"> <span class="number">3</span>	     <span class="number">5</span>NN50	<span class="number">0.021</span></span><br><span class="line"> <span class="number">4</span>	   <span class="number">5</span>R_mean	<span class="number">0.019</span></span><br><span class="line"> <span class="number">5</span>	   <span class="number">5</span>p_skew	<span class="number">0.019</span></span><br><span class="line"> <span class="number">6</span>	       <span class="number">5</span>HF	<span class="number">0.018</span></span><br><span class="line"> <span class="number">7</span>	   <span class="number">5</span>HR_min	<span class="number">0.017</span></span><br><span class="line"> <span class="number">8</span>	 <span class="number">5</span>p_median	<span class="number">0.017</span></span><br><span class="line"> <span class="number">9</span>	 <span class="number">5</span>R_median	<span class="number">0.017</span></span><br><span class="line"><span class="number">10</span>	     <span class="number">5</span>apen	<span class="number">0.017</span></span><br><span class="line"><span class="number">11</span>	  <span class="number">5</span>HR_mean	<span class="number">0.017</span></span><br><span class="line"><span class="number">12</span>	  <span class="number">5</span>p_RMSSD	<span class="number">0.016</span></span><br><span class="line"><span class="number">13</span>	   <span class="number">5</span>R_CVSD	<span class="number">0.016</span></span><br><span class="line"><span class="number">14</span>	    <span class="number">5</span>csi10	<span class="number">0.016</span></span><br><span class="line"><span class="number">15</span>	   <span class="number">5</span>p_SDNN	<span class="number">0.016</span></span><br><span class="line"><span class="number">16</span>	    <span class="number">5</span>p_RMS	<span class="number">0.016</span></span><br><span class="line"><span class="number">17</span>	    <span class="number">5</span>p_max	<span class="number">0.016</span></span><br><span class="line"><span class="number">18</span>	    <span class="number">5</span>p_var	<span class="number">0.015</span></span><br><span class="line"><span class="number">19</span>	   <span class="number">5</span>p_mean	<span class="number">0.015</span></span><br><span class="line"><span class="number">20</span>	   <span class="number">5</span>R_SDSD	<span class="number">0.015</span></span><br><span class="line"><span class="number">21</span>	  <span class="number">5</span>R_RMSSD	<span class="number">0.015</span></span><br></pre></td></tr></table></figure>

<p>排序顺序为543分类</p>
<p>直接用一个行表格不就行了嘛。。。</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/6/18</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 选定固定的特征值</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"><span class="comment"># 忽略警告</span></span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># for h in range(1, 4):</span></span><br><span class="line"><span class="comment">#     feature_import = pd.read_excel(&#x27;E:/features&#x27; + &#x27;/feature_important&#x27; + &#x27;%s&#x27; % h + &#x27;.xlsx&#x27;)</span></span><br><span class="line"><span class="comment">#     df = pd.get_dummies(feature_import.iloc[0:len(feature_import), 1:23])</span></span><br><span class="line"><span class="comment">#     data = df.T</span></span><br><span class="line"><span class="comment">#     print(f&#x27;列写特征重要度排序&#x27;, end=&#x27;\t&#x27;)</span></span><br><span class="line"><span class="comment">#     print()</span></span><br><span class="line"><span class="comment">#     print(f&#x27;序号&#x27;, end=&#x27;\t\t&#x27;)</span></span><br><span class="line"><span class="comment">#     print(f&#x27;特征&#x27;, end=&#x27;\t\t&#x27;)</span></span><br><span class="line"><span class="comment">#     print(f&#x27;重要度&#x27;)</span></span><br><span class="line"><span class="comment">#     for i in range(22):</span></span><br><span class="line"><span class="comment">#         print(f&#x27;%2d&#x27; % i, end=&#x27;\t&#x27;)</span></span><br><span class="line"><span class="comment">#         print(f&#x27;%10s&#x27; % df.keys()[i], end=&#x27;\t&#x27;)</span></span><br><span class="line"><span class="comment">#         print(f&#x27;%0.3f&#x27; % data[0][i])</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">22</span>):</span><br><span class="line">    <span class="keyword">for</span> h <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">4</span>):</span><br><span class="line">        feature_import = pd.read_excel(<span class="string">&#x27;E:/features&#x27;</span> + <span class="string">&#x27;/feature_important&#x27;</span> + <span class="string">&#x27;%s&#x27;</span> % h + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">        df = pd.get_dummies(feature_import.iloc[<span class="number">0</span>:<span class="built_in">len</span>(feature_import), <span class="number">1</span>:<span class="number">23</span>])</span><br><span class="line">        data = df.T</span><br><span class="line">        print(<span class="string">f&#x27;%15s&#x27;</span> % df.keys()[i], end=<span class="string">&#x27;\t&#x27;</span>)</span><br><span class="line">    print()</span><br><span class="line"></span><br><span class="line"></span><br></pre></td></tr></table></figure>



<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line">	   5期			  4期			 3期</span><br><span class="line">	 5pNN20	          5NN20	         5pNN20	</span><br><span class="line">         5NN20	         5pNN20	          5NN20	</span><br><span class="line">        5pNN50	         5pNN50	         5pNN50	</span><br><span class="line">         5NN50	          5NN50	          5NN50	</span><br><span class="line">      5p_RMSSD	        5p_skew	        5R_mean	</span><br><span class="line">        5p_var	            5HF	        5p_skew	</span><br><span class="line">        5csi50	      5p_median	            5HF	</span><br><span class="line">        5csi30	        5R_mean	        5HR_min	</span><br><span class="line">        5csi10	        5p_SDNN	      5p_median	</span><br><span class="line">           5HF	         5p_var	      5R_median	</span><br><span class="line">       5p_skew	         5p_RMS	          5apen	</span><br><span class="line">       5p_SDNN	         5csi10	       5HR_mean	</span><br><span class="line">     5p_median	       5p_RMSSD	       5p_RMSSD	</span><br><span class="line">       5R_mean	        5R_CVSD	        5R_CVSD	</span><br><span class="line">      5HR_mean	         5p_max	         5csi10	</span><br><span class="line">        5p_RMS	         5csi50	        5p_SDNN	</span><br><span class="line">       5p_mean	        5R_SDSD	         5p_RMS	</span><br><span class="line">        5p_max	        5p_mean	         5p_max	</span><br><span class="line">     5R_median	           5sd1	         5p_var	</span><br><span class="line">         5apen	       5R_RMSSD	        5p_mean	</span><br><span class="line">5p_peak_factor	        5HR_min	        5R_SDSD	</span><br><span class="line">       5csi100	          5apen	       5R_RMSSD	</span><br></pre></td></tr></table></figure>

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